We adopt the vision transformer [1]} for its efficiency and flexibility in handling input content of variable size. The vision transformer embeds the input visual tokens into a distributed representation via the self-attention mechanism. An instance representation is desired in order to allow supervision from the instance level. To achieve this, we use cross-attention blocks [2]} to aggregate the distributed patch-level representations into a single representation with an additional appended class token. The distributed representations are frozen without updates, and this makes the cross-attention blocks both efficient and lightweight. The projection head and the predictor follow the instance representation.
